Apparatus and methods for credentialing users across user devices

ABSTRACT

A apparatus for credentialing users across multiple devices. The apparatus includes a processor connected to a network and at least a user device. Processor is configured to receive a credential data structure, verify the credential data structure, generate a credential block, and store the credential block in a data storage system. A plurality of user devices may access the network and the data storage system to view the verified credentials.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Nonprovisional application Ser.No. 17/667,512, filed on Feb. 8, 2022, and entitled “APPARATUS ANDMETHODS FOR CREDENTIALING USERS ACROSS USER DEVICES,” the entirety ofwhich is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention generally relates to the field of cryptography. Inparticular, the present invention is directed to apparatuses and methodscredentialing users across user devices.

BACKGROUND

User data may be stored securely using cryptography. However, user datamay need to be verified. There is a need for an apparatus and methodthat can verify and store the verification on a network accessible by aplurality of user devices.

SUMMARY OF THE DISCLOSURE

In an aspect an apparatus for credentialing users across multipledevices includes at least a processor and a memory communicativelyconnected to the processor, the memory containing instructionsconfiguring the at least a processor to receive a credential datastructure from a user by use of an identifier, verify the credentialdata structure, wherein verifying further includes parsing at least acredential from the credential data structure, generating a validatorcommunity set as a function of the at least a credential, wherein thevalidator community set includes a plurality of identifiers andtransmitting a validation request to a third-party validator to a remotedevice associated with an identifier of the plurality of identifiers andgenerating a web crawling process for the third-party validator.

In another aspect a method for credentialing users across multipledevices includes receiving, by a processor a credential data structureby use of an identifier, verifying, by the processor, the credentialdata structure, parsing, by the processor, at least a credential fromthe credential data structure, generating, by the processor, a validatorcommunity set as a function of the at least a credential, wherein thevalidator community set includes a plurality of identifiers,transmitting, by the processor, a validation request to a third-partyvalidator to a remote device associated with an identifier of theplurality of identifiers, generating a web crawling process for thethird-party validator and receiving, by the processor, a validationrecord from the third-party validator of the remote device.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of anapparatus for credentialing users;

FIG. 2 is a block diagram illustrating an exemplary embodiment of animmutable sequential listing;

FIG. 3 is a block diagram illustrating an exemplary embodiment of acryptographic accumulator;

FIG. 4 is a flow diagram illustrating an exemplary embodiment of amethod for credentialing users;

FIG. 5 is a block diagram illustrating an exemplary embodiment of amachine-learning module; and

FIG. 6 is a block diagram of a computing apparatus that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed toapparatuses and methods for credentialing users across multiple devices.In an embodiment, employers may have a desire to verify qualificationsof a job applicant.

Aspects of the present disclosure can be used to validate qualificationsand credentials of a user such as a job applicant. Aspects of thepresent disclosure can also be used to store the validatedqualifications on an immutable sequential listing such that a pluralityof user devices may access the data. Exemplary embodiments illustratingaspects of the present disclosure are described below in the context ofseveral specific examples.

In an embodiment, methods and apparatuses described herein may performor implement one or more aspects of a cryptographic apparatus. In oneembodiment, a cryptographic apparatus is an apparatus that converts datafrom a first form, known as “plaintext,” which is intelligible whenviewed in its intended format, into a second form, known as“cyphertext,” which is not intelligible when viewed in the same way.Cyphertext may be unintelligible in any format unless first convertedback to plaintext. In one embodiment, a process of converting plaintextinto cyphertext is known as “encryption.” Encryption may involve the useof a datum, known as an “encryption key,” to alter plaintext.Cryptographic apparatus may also convert cyphertext back into plaintext,which is a process known as “decryption.” Decryption process may involvethe use of a datum, known as a “decryption key,” to return thecyphertext to its original plaintext form. In embodiments ofcryptographic apparatus that are “symmetric,” decryption key isessentially the same as encryption key: possession of either key makesit possible to deduce the other key quickly without further secretknowledge. Encryption and decryption keys in symmetric cryptographicapparatus may be kept secret and shared only with persons or entitiesthat the user of the cryptographic apparatus wishes to be able todecrypt the cyphertext. One example of a symmetric cryptographicapparatus is the Advanced Encryption Standard (“AES”), which arrangesplaintext into matrices and then modifies the matrices through repeatedpermutations and arithmetic operations with an encryption key.

In embodiments of cryptographic systems that are “asymmetric,” eitherencryption or decryption key cannot be readily deduced withoutadditional secret knowledge, even given the possession of acorresponding decryption or encryption key, respectively; a commonexample is a “public key cryptographic apparatus,” in which possessionof the encryption key does not make it practically feasible to deducethe decryption key, so that the encryption key may safely be madeavailable to the public. An example of a public key cryptographicapparatus is RSA, in which an encryption key involves the use of numbersthat are products of very large prime numbers, but a decryption keyinvolves the use of those very large prime numbers, such that deducingthe decryption key from the encryption key requires the practicallyinfeasible task of computing the prime factors of a number which is theproduct of two very large prime numbers. A further example of anasymmetric cryptographic apparatus may include a discrete-logarithmbased apparatus based upon the relative ease of computing exponents moda large integer, and the computational infeasibility of determining thediscrete logarithm of resulting numbers absent previous knowledge of theexponentiations; an example of such an apparatus may includeDiffie-Hellman key exchange and/or public key encryption. Anotherexample is elliptic curve cryptography, which relies on the fact thatgiven two points P and Q on an elliptic curve over a finite field, adefinition of the inverse of a point −A as the point with negativey-coordinates, and a definition for addition where A+B=−R, the pointwhere a line connecting point A and point B intersects the ellipticcurve, where “0,” the identity, is a point at infinity in a projectiveplane containing the elliptic curve, finding a number k such that addingP to itself k times results in Q is computationally impractical, givencorrectly selected elliptic curve, finite field, and P and Q. A furtherexample of asymmetrical cryptography may include lattice-basedcryptography, which relies on the fact that various properties of setsof integer combination of basis vectors are hard to compute, such asfinding the one combination of basis vectors that results in thesmallest Euclidean distance. Embodiments of cryptography, whethersymmetrical or asymmetrical, may include quantum-secure cryptography,defined for the purposes of this disclosure as cryptography that remainssecure against adversaries possessing quantum computers; some forms oflattice-based cryptography, for instance, may be quantum-secure.

In some embodiments, apparatuses and methods described herein producecryptographic hashes, also referred to by the equivalent shorthand term“hashes.” A cryptographic hash, as used herein, is a mathematicalrepresentation of a lot of data, such as files or blocks in a blockchain as described in further detail below; the mathematicalrepresentation is produced by a lossy “one-way” algorithm known as a“hashing algorithm.” Hashing algorithm may be a repeatable process; thatis, identical lots of data may produce identical hashes each time theyare subjected to a particular hashing algorithm. Because hashingalgorithm is a one-way function, it may be impossible to reconstruct alot of data from a hash produced from the lot of data using the hashingalgorithm. In the case of some hashing algorithms, reconstructing thefull lot of data from the corresponding hash using a partial set of datafrom the full lot of data may be possible only by repeatedly guessing atthe remaining data and repeating the hashing algorithm; it is thuscomputationally difficult if not infeasible for a single computer toproduce the lot of data, as the statistical likelihood of correctlyguessing the missing data may be extremely low. However, the statisticallikelihood of a computer of a set of computers simultaneously attemptingto guess the missing data within a useful timeframe may be higher,permitting mining protocols as described in further detail below.

In an embodiment, hashing algorithm may demonstrate an “avalancheeffect,” whereby even extremely small changes to lot of data producedrastically different hashes. This may thwart attempts to avoid thecomputational work necessary to recreate a hash by simply inserting afraudulent datum in data lot, enabling the use of hashing algorithms for“tamper-proofing” data such as data contained in an immutable ledger asdescribed in further detail below. This avalanche or “cascade” effectmay be evinced by various hashing processes; persons skilled in the art,upon reading the entirety of this disclosure, will be aware of varioussuitable hashing algorithms for purposes described herein. Verificationof a hash corresponding to a lot of data may be performed by running thelot of data through a hashing algorithm used to produce the hash. Suchverification may be computationally expensive, albeit feasible,potentially adding up to significant processing delays where repeatedhashing, or hashing of large quantities of data, is required, forinstance as described in further detail below. Examples of hashingprograms include, without limitation, SHA256, a NIST standard; furthercurrent and past hashing algorithms include Winternitz hashingalgorithms, various generations of Secure Hash Algorithm (including“SHA-1,” “SHA-2,” and “SHA-3”), “Message Digest” family hashes such as“MD4,” “MD5,” “MD6,” and “RIPEMD,” Keccak, “BLAKE” hashes and progeny(e.g., “BLAKE2,” “BLAKE-256,” “BLAKE-512,” and the like), MessageAuthentication Code (“MAC”)-family hash functions such as PMAC, OMAC,VMAC, HMAC, and UMAC, Poly1305-AES, Elliptic Curve Only Hash (“ECOH”)and similar hash functions, Fast-Syndrome-based (FSB) hash functions,GOST hash functions, the Grostl hash function, the HAS-160 hashfunction, the JH hash function, the RadioGatnn hash function, the Skeinhash function, the Streebog hash function, the SWIFFT hash function, theTiger hash function, the Whirlpool hash function, or any hash functionthat satisfies, at the time of implementation, the requirements that acryptographic hash be deterministic, infeasible to reverse-hash,infeasible to find collisions, and have the property that small changesto an original message to be hashed will change the resulting hash soextensively that the original hash and the new hash appear uncorrelatedto each other. A degree of security of a hash function in practice maydepend both on the hash function itself and on characteristics of themessage and/or digest used in the hash function. For example, where amessage is random, for a hash function that fulfillscollision-resistance requirements, a brute-force or “birthday attack”may to detect collision may be on the order of O(2^(n/2)) for n outputbits; thus, it may take on the order of 2²⁵⁶ operations to locate acollision in a 512 bit output “Dictionary” attacks on hashes likely tohave been generated from a non-random original text can have a lowercomputational complexity, because the space of entries they are guessingis far smaller than the space containing all random permutations ofbits. However, the space of possible messages may be augmented byincreasing the length or potential length of a possible message, or byimplementing a protocol whereby one or more randomly selected strings orsets of data are added to the message, rendering a dictionary attacksignificantly less effective.

Embodiments of apparatuses and methods described herein may generate,evaluate, and/or utilize digital signatures. A “digital signature,” asused herein, includes a secure proof of possession of a secret by asigning device, as performed on provided element of data, known as a“message.” A message may include an encrypted mathematicalrepresentation of a file or other set of data using the private key of apublic key cryptographic apparatus. Secure proof may include any form ofsecure proof as described in further detail below, including withoutlimitation encryption using a private key of a public key cryptographicapparatus as described above. Signature may be verified using averification datum suitable for verification of a secure proof; forinstance, where secure proof is enacted by encrypting message using aprivate key of a public key cryptographic apparatus, verification mayinclude decrypting the encrypted message using the corresponding publickey and comparing the decrypted representation to a purported match thatwas not encrypted; if the signature protocol is well-designed andimplemented correctly, this means the ability to create the digitalsignature is equivalent to possession of the private decryption keyand/or device-specific secret. Likewise, if a message making up amathematical representation of file is well-designed and implementedcorrectly, any alteration of the file may result in a mismatch with thedigital signature; the mathematical representation may be produced usingan alteration-sensitive, reliably reproducible algorithm, such as ahashing algorithm as described above. A mathematical representation towhich the signature may be compared may be included with signature, forverification purposes; in other embodiments, the algorithm used toproduce the mathematical representation may be publicly available,permitting the easy reproduction of the mathematical representationcorresponding to any file.

In some embodiments, digital signatures may be combined with orincorporated in digital certificates. In one embodiment, a digitalcertificate is a file that conveys information and links the conveyedinformation to a “certificate authority” that is the issuer of a publickey in a public key cryptographic apparatus. Certificate authority insome embodiments contains data conveying the certificate authority'sauthorization for the recipient to perform a task. The authorization maybe the authorization to access a given datum. The authorization may bethe authorization to access a given process. In some embodiments, thecertificate may identify the certificate authority. The digitalcertificate may include a digital signature.

In some embodiments, a third-party such as a certificate authority (CA)is available to verify that the possessor of the private key is aparticular entity; thus, if the certificate authority may be trusted,and the private key has not been stolen, the ability of an entity toproduce a digital signature confirms the identity of the entity andlinks the file to the entity in a verifiable way. Digital signature maybe incorporated in a digital certificate, which is a documentauthenticating the entity possessing the private key by authority of theissuing certificate authority and signed with a digital signaturecreated with that private key and a mathematical representation of theremainder of the certificate. In other embodiments, digital signature isverified by comparing the digital signature to one known to have beencreated by the entity that purportedly signed the digital signature; forinstance, if the public key that decrypts the known signature alsodecrypts the digital signature, the digital signature may be consideredverified. Digital signature may also be used to verify that the file hasnot been altered since the formation of the digital signature.

Referring now to FIG. 1 , an exemplary embodiment of an apparatus 100for credentialing users across multiple devices is illustrated.Apparatus 100 includes a processor 104. Processor 104 is communicativelyconnected to a network 128 including at least a user device 132.Processor 104 may include any processor 104 as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or apparatus on achip (SoC) as described in this disclosure. Processor 104 may include,be included in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Processor 104 may include a single processoroperating independently, or may include two or more processor operatingin concert, in parallel, sequentially or the like; two or moreprocessors may be included together in a single processor or in two ormore processors. Processor 104 may interface or communicate with one ormore additional devices as described below in further detail via anetwork interface device. Network interface device may be utilized forconnecting processor 104 to one or more of a variety of networks, andone or more devices. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two processors, and any combinations thereof. A network mayemploy a wired and/or a wireless mode of communication. In general, anynetwork topology may be used. Information (e.g., data, software etc.)may be communicated to and/or from a computer and/or a processor.Processor 104 may include but is not limited to, for example, aprocessor or cluster of processors in a first location and a secondprocessor or cluster of processors in a second location. Processor 104may include one or more processors dedicated to data storage, security,distribution of traffic for load balancing, and the like. Processor 104may distribute one or more computing tasks as described below across aplurality of processors of processor 104, which may operate in parallel,in series, redundantly, or in any other manner used for distribution oftasks or memory between processors. Processor 104 may be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of apparatus 100and/or processor 104.

With continued reference to FIG. 1 , processor 104 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, processor 104 maybe configured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Processor 104 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

With continued reference to FIG. 1 , apparatus 100 includes at least auser device 132 and a user 108. As used herein, a “user device,” mayrefer to any device that may connect with the apparatus 100, processor104, network, and/or data storage system 124, as described in furtherdetail below. In an embodiment, a user device 132 may includesmartphones, computers, tablets, and the like. A user 108 may includejob seekers and job offerers. As used herein, a “job seeker” is a personlooking for a job. For example, a job seeker may be a potentialapplicant or candidate for a job. As used herein, a “job offerer” is aperson that is looking for a job seeker to fill a position. For example,a job offerer may include recruiters, employers, hiring managers, jobaggregators, job posting boards, and the like. A job offerer may be anentity such as a staffing agency, hiring department of employers,insurers, government agencies, and the like.

With continued reference to FIG. 1 , apparatus 100 may include a network128. For example and without limitation, a network 128 may include amesh network, a server, a cloud, hub, etc. The network 128 may connect aplurality of user devices such that they may be in an ecosystem. An“ecosystem” as used herein, is the network of user devices that arecommunicatively connected. An ecosystem may share user credentialsacross a plurality user devices. In another embodiment, the ecosystemmay include job offerers devices and job seekers devices such that a jobofferer may look at job seeker credentials. Credential data structure112 includes user qualifications. As used herein, “credentials” are theuser's qualifications and identifiers. In an embodiment, credentials mayinclude licenses, job titles, locations, references, records, education,awards, recognitions, and the like. Credentials may be stored in acredential data structure 112, wherein a credential data structure 112may be a digital wallet.

With continued reference to FIG. 1 , processor 104 is configured toreceive a credential data structure 112 from a user 108 and verify thecredential data structure 112. Processor 104 may receive a credentialdata structure 112 from a user 108 by use of an identifier. As usedherein, an “identifier” is a public key or a piece of data based on thepublic key that associates a user 108 to the block of information. Anidentifier may include, in the context of a job board, an email address,a home address, etc. An identifier may also include, IP addresses,domain names, etc. Once processor 104 receives the credential datastructure 112, processor 104 is configured to parse at least acredential from the credential data structure. At least a credential mayinclude a user qualification that needs to be validated. For example, auser's credential data structure 112 may be associated with a particularbackground a user may have, such as a law background. In this case,processor 104 may parse out specific qualifications in the credentialdata structure related to law that need to be verified such as theuser's law degree from a university, the user's bar certification, theuser's jobs that have been held in the law field, and the like.Processor 104 may parse at least a credential from the credential datastructure 112 by utilizing the blocks in the credential data structure112, wherein each block represents a separate credential within thewallet. Credential data structure 112 may be stored on an immutablesequential listing discussed in further detail below.

With continued reference to FIG. 1 , processor 104 is configured togenerate a validator community set 136 as a function of the at least acredential, wherein the validator community set 136 includes a pluralityof identifiers. As used herein, a “validator community set” is a set ofcomputing devices capable of validating a credential and/or operated bypeople that could validate a credential; validator community set 136also may include identifiers associated with the set of devices and/orpeople. An identifier may include any identifier listed in thisdisclosure. For example, a juris doctor degree may be validated by a setof people within the university that the degree was obtained from. Avalidator community set 136 may include, without limitation, peopleand/or devices associated with and/or belonging to an admissionsdepartment, a law department, or the like. In another embodiment,validating a specific job may be achieved by a set of people that workat a job and/or devices thereof. For example, a human resourcesdepartment, or a manager of user 108 may validate accuracy of a joband/or job title. A machine-learning module may be used to identifyvalidator community set 136. A machine learning module 500 may use amachine learning process. A machine learning process, also referred toas a machine-learning algorithm, is a process that automatedly usestraining data and/or a training set as described below to generate analgorithm that will be performed by a processor 104 and/or module toproduce outputs given data provided as inputs; this is in contrast to anon-machine learning software program where the commands to be executedare determined in advance by a user 108 and written in a programminglanguage. Machine learning module is described in further detail in FIG.5 . Machine learning process may be trained using training data,described in further detail in FIG. 5 , to input credentials and avalidator community set 136 related to the credentials. In anembodiment, machine learning process may generate a validator communityset 136 of people in a company that may validate a user's previous jobat said company. In another embodiment, machine learning process maygenerate a validator community set 136 of officials at a university thatmay validate a degree. Processor 104 may generate classifier using aclassification algorithm, defined as a processes whereby a processor 104derives, from training data, a model known as a “classifier” for sortinginputs into categories or bins of data. Classification may be performedusing, without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers. Classifier may categorize credentials of auser 108 and/or validator community sets, and the like. Training datamay include previously classified credentials and validator communitysets.

Processor 104 may verify the credential data structure 112 usingthird-party validator 116. Processor 104 is configured to transmit avalidation request to a remote device associated with an identifier of aplurality of identifiers. Third-party validator 116 may include one ormore remote devices in communication with the processor 104. At least athird-party validator 116 may include modules such as cryptographicmodule, and/or key retrieval module. At least a third-party validator116 may be operated by a job offeror. A job offeror may include athird-party found in the validator community set 136 who may have arelationship with a job seeker and who may validate informationpertaining to job seeker. For example, this may include an individualwho may have worked with job seeker in the past, or who may currentlywork with job seeker. This may also include peers such as a mentor thatjob seeker may have interned for. Third-party may be an authorizedperson from an organization job seeker volunteered at or may be a hiringmanager or human resources manager who kept employment recordspertaining to job seeker. A third-party may validate informationpertaining to job seeker such as employment history, job seekerdemographics, education, skills, social activities, and/or academicdetails. In an embodiment, a third-party may include social mediasources instead of a person who is able to verify information pertainingto a job seeker. For example, a third-party may include a processor thatmay engage in web crawling to confirm job seeker activity in socialengagements such as by checking websites of organizations and clubswhere job seeker may engage in social engagements. For example, aprocessor may verify if a job seeker was a volunteer at job seeker'schurch by web crawling to requester's church website and examining thewebsite to see if job seeker may be listed as a volunteer. Web crawlingmay include checking related websites and other sources of informationthat may indicate clues in reference to job seeker social engagements,for example. Key retrieval module may include one or more components ofhardware and/or software program code for retrieving, obtaining, orotherwise receiving, and/or processing a public key and/or an encryptedprivate key from a job seeker. In an embodiment, this may include apublic key and an private key, which may include an encrypted privatekey generated from a biometric feature of a job seeker, from secret dataprovided by job seeker or by any other suitable means for generation ofa private or public key; encrypted private key may be encrypted usingany cryptographic apparatus as described above, including withoutlimitation a cryptographic apparatus using additional private or publickeys generated from biometric features and/or secret data of job seeker.Key retrieval module may also store for example, a public key associatedwith a job seeker for later use within apparatus 100, such as whencryptographic module or other devices and/or modules within or incommunication with the apparatus 100 may need to encrypt a message usingjob seeker's public key. Key retrieval module may also store a jobseeker's private key for later use within apparatus 100. For example,key retrieval module may store an encrypted private key associated witha job seeker. The encrypted private key may be decrypted by a biometricsignature of job seeker generated by biometric reader, and/or othersignature generated using secret data of job seeker as described above.In an embodiment, a public key may be utilized to encrypt a messagewhile the message can only be decrypted using a private key. In anembodiment, a public key may be widely distributed, while a private keymay be known only to its proprietor. In an embodiment, key retrievalmodule may store an encrypted private key that may only be decryptedusing a biometric sample from a job seeker and/or other secret data fromjob seeker. In yet another non-limiting embodiment, a biometric sampleand/or other secret data may be used to generate the private key and thebiometric sample and/or other secret data may be used to decrypt theprivate key. Additional disclosure related to third-party validators canbe found in U.S. patent application Ser. No. 16/271,521 entitled“APPARATUS AND METHODS FOR BIOMETRIC KEY GENERATION IN DATA ACCESSCONTROL, DATA VERIFICATION, AND PATH SELECTION IN BLOCK CHAIN-LINKEDWORKFORCE DATA MANAGEMENT” and filed on Feb. 8, 2019, entirety of whichin incorporated herein by reference.

With continued reference to FIG. 1 , user qualification verificationincludes using public key decryption. User qualification includesencryption using a private key. In an embodiment, an university, who maybe a part of a validator community set 136, may encrypt a user's degreeusing the university's private key and/or digital signature such that athird-party validator 116 and/or processor 104 may verify the user'sdegree by decryption using the university's public key. Similar privatekey encryption and public key decryptions may be applied to othercredentials the user 108 may store in the credential data structure 112as discussed above. In an embodiment, a similar process may be appliedto a user's titles such as Doctor of Medicine (MD) or Juris Doctor (JD).Processor 104 and/or third-party validator 116 may validate a user'scredentials to ensure that they qualify for job postings. A job postingmay be looking for job seekers that have a JD and may utilize apparatus100 to authenticate the job seeker.

Still referring to FIG. 1 , apparatus 100 may include a data integrityvalidator. Data integrity validator may be implemented as any hardwareor software module as described above. Data integrity validator 116 maybe designed and configured to perform any embodiment of any process stepand/or set of process steps, including without limitation as describedherein in reference to FIG. 4 . For instance, and without limitation,data integrity validator operating on processor 104 may be designed andconfigured to validate credentials, wherein validating further comprisestransmitting, to at least a third-party validator device of a validatorcommunity set 136 a validation request, receiving, from at least athird-party validator device a validation record including a third-partydigital signature validating the credential, authenticating thethird-party digital signature, and validating the credential as afunction of the validation record. As a further non-limiting example,data integrity validator may be designed and configured to validatecredentials, wherein validating further comprises transmitting to the atleast a third-party validator device of credential data structure a +request, the validation providing access to a credential data structureto the at least a third-party validator device, receiving from the atleast a third-party validator device a validation record including athird-party digital signature validating the credential data structure,authenticating the third-party digital signature, and validating thecredential data structure as a function of the validation record.

Additional disclosure related to validation of credentials can be foundin U.S. patent application Ser. No. 17/486,461 entitled “SYSTEMS ANDMETHODS FOR SCORE GENERATION FOR APPLICANT TRACKING” and filed on Sep.27, 2021, entirety of which in incorporated herein by reference.

Still referring to FIG. 1 , processor 104 is configured to validate useridentity associated with the credential data structure 112. In additionto validating the user qualifications, etc. within the credential datastructure 112, processor 104 is configured to validate the user identityassociated with the credential data structure 112. Processor 104 mayrequest a user-specific secret. A user-specific secret (also referred toas “secret”) comprises any data that is only known by or only possessedby the user 108. For example, a secret may include a password, apersonal identification number, a mnemonic device, etc. The secret maybe linked to the credential data structure 112 with a cryptographiccommitment. The cryptographic commitment includes a Pederson commitment.A “Pederson commitment”, as used herein, is a cryptographic algorithmthat allows the user 108 to commit to a certain value without revealingit. For example, a user 108 may be required to enter the user-specificsecret as a commitment. This may be used to verify user identity toprove possession of an identifier later on when the commitment isopened. A cryptographic commitment may additionally or alternativelyinclude a cryptographic hash of the user-specific secret and/or acryptographic accumulator such as a Merkle tree of the user-specificsecret. In an example where a user password is the user-specific secret,a hash of the commitment may be compared to the hash of the actual userpassword to verify user identity. User identity may additionally beverified using a location-based IP address. In an embodiment, a userapplying to jobs in Los Angeles, Calif. typically would not have an IPaddress based in Oslo, Norway. User identity may additionally beverified using two-factor authentication. In an embodiment, the user mayget a verification request on another processor 104 known to be owned bythe user 108. In an embodiment, proof of identity may include a secureproof of possession of at least a portion of the data. User verificationmay be completed using a one or a combination of verification methodslisted above.

Continuing to refer to FIG. 1 , a “secure proof,” as used in thisdisclosure, is a protocol whereby an output is generated thatdemonstrates possession of a secret, such as device-specific secret,without demonstrating the entirety of the device-specific secret; inother words, a secure proof by itself, is insufficient to reconstructthe entire device-specific secret, enabling the production of at leastanother secure proof using at least a device-specific secret. A secureproof may be referred to as a “proof of possession” or “proof ofknowledge” of a secret. Where at least a device-specific secret is aplurality of secrets, such as a plurality of challenge-response pairs, asecure proof may include an output that reveals the entirety of one ofthe plurality of secrets, but not all of the plurality of secrets; forinstance, secure proof may be a response contained in onechallenge-response pair. In an embodiment, proof may not be secure; inother words, proof may include a one-time revelation of at least adevice-specific secret, for instance as used in a singlechallenge-response exchange.

Secure proof may include a zero-knowledge proof, which may provide anoutput demonstrating possession of a secret while revealing none of thesecret to a recipient of the output; zero-knowledge proof may beinformation-theoretically secure, meaning that an entity with infinitecomputing power would be unable to determine secret from output.Alternatively, zero-knowledge proof may be computationally secure,meaning that determination of secret from output is computationallyinfeasible, for instance to the same extent that determination of aprivate key from a public key in a public key cryptographic apparatus iscomputationally infeasible. Zero-knowledge proof algorithms maygenerally include a set of two algorithms, a prover algorithm, or “P,”which is used to prove computational integrity and/or possession of asecret, and a verifier algorithm, or “V” whereby a party may check thevalidity of P. Zero-knowledge proof may include an interactivezero-knowledge proof, wherein a party verifying the proof must directlyinteract with the proving party; for instance, the verifying and provingparties may be required to be online, or connected to the same networkas each other, at the same time. Interactive zero-knowledge proof mayinclude a “proof of knowledge” proof, such as a Schnorr algorithm forproof on knowledge of a discrete logarithm. in a Schnorr algorithm, aprover commits to a randomness r, generates a message based on r, andgenerates a message adding r to a challenge c multiplied by a discretelogarithm that the prover is able to calculate; verification isperformed by the verifier who produced c by exponentiation, thuschecking the validity of the discrete logarithm. Interactivezero-knowledge proofs may alternatively or additionally include sigmaprotocols. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various alternative interactivezero-knowledge proofs that may be implemented consistently with thisdisclosure.

Alternatively, zero-knowledge proof may include a non-interactivezero-knowledge, proof, or a proof wherein neither party to the proofinteracts with the other party to the proof, for instance, each of aparty receiving the proof and a party providing the proof may receive areference datum which the party providing the proof may modify orotherwise use to perform the proof. As a non-limiting example,zero-knowledge proof may include a succinct non-interactive arguments ofknowledge (ZK-SNARKS) proof, wherein a “trusted setup” process createsproof and verification keys using secret (and subsequently discarded)information encoded using a public key cryptographic apparatus, a proverruns a proving algorithm using the proving key and secret informationavailable to the prover, and a verifier checks the proof using theverification key; public key cryptographic apparatus may include RSA,elliptic curve cryptography, ElGamal, or any other suitable public keycryptographic apparatus. Generation of trusted setup may be performedusing a secure multiparty computation so that no one party has controlof the totality of the secret information used in the trusted setup; asa result, if any one party generating the trusted setup is trustworthy,the secret information may be unrecoverable by malicious parties. Asanother non-limiting example, non-interactive zero-knowledge proof mayinclude a Succinct Transparent Arguments of Knowledge (ZK-STARKS)zero-knowledge proof. In an embodiment, a ZK-STARKS proof includes aMerkle root of a Merkle tree representing evaluation of a secretcomputation at some number of points, which may be 1 billion points,plus Merkle branches representing evaluations at a set of randomlyselected points of the number of points; verification may includedetermining that Merkle branches provided match the Merkle root, andthat point verifications at those branches represent valid values, wherevalidity is shown by demonstrating that all values belong to the samepolynomial created by transforming the secret computation. In anembodiment, ZK-STARKS does not require a trusted setup.

Zero-knowledge proof may include any other suitable zero-knowledge proofZero-knowledge proof may include, without limitation bulletproofs.Zero-knowledge proof may include a homomorphic public-key cryptography(hPKC)-based proof Zero-knowledge proof may include a discretelogarithmic problem (DLP) proof. Zero-knowledge proof may include asecure multi-party computation (MPC) proof Zero-knowledge proof mayinclude, without limitation, an incrementally verifiable computation(IVC). Zero-knowledge proof may include an interactive oracle proof(IOP). Zero-knowledge proof may include a proof based on theprobabilistically checkable proof (PCP) theorem, including a linear PCP(LPCP) proof. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various forms of zero-knowledge proofsthat may be used, singly or in combination, consistently with thisdisclosure.

In an embodiment, secure proof is implemented using a challenge-responseprotocol. In an embodiment, this may function as a one-time padimplementation; for instance, a manufacturer or other trusted party mayrecord a series of outputs (“responses”) produced by a device possessingsecret information, given a series of corresponding inputs(“challenges”), and store them securely. In an embodiment, achallenge-response protocol may be combined with key generation. Asingle key may be used in one or more digital signatures as described infurther detail below, such as signatures used to receive and/or transferpossession of crypto-currency assets; the key may be discarded forfuture use after a set period of time. In an embodiment, varied inputsinclude variations in local physical parameters, such as fluctuations inlocal electromagnetic fields, radiation, temperature, and the like, suchthat an almost limitless variety of private keys may be so generated.Secure proof may include encryption of a challenge to produce theresponse, indicating possession of a secret key. Encryption may beperformed using a private key of a public key cryptographic apparatus,or using a private key of a symmetric cryptographic apparatus; forinstance, trusted party may verify response by decrypting an encryptionof challenge or of another datum using either a symmetric or public-keycryptographic apparatus, verifying that a stored key matches the keyused for encryption as a function of at least a device-specific secret.Keys may be generated by random variation in selection of prime numbers,for instance for the purposes of a cryptographic apparatus such as RSAthat relies prime factoring difficulty. Keys may be generated byrandomized selection of parameters for a seed in a cryptographicapparatus, such as elliptic curve cryptography, which is generated froma seed. Keys may be used to generate exponents for a cryptographicapparatus such as Diffie-Helman or ElGamal that are based on thediscrete logarithm problem.

Continuing to reference FIG. 1 , user 108 may possess a plurality ofcredential data structures. In an embodiment, credential data structuresmay be role specific such that different credential data structures maybe used to apply to different types of jobs/roles. For example, a user108 may have a bartending background and a law background. User 108 mayuse one credential data structure to apply to bartending jobs and adifferent credential data structure to apply to law jobs. Userqualifications may overlap in credential data structures. Differentcredential data structures may also have unique credentials. Eachcredential data structure may include a digital signature or anidentifier to distinguish the credential data structures.

With continued reference to FIG. 1 , processor 104 is configured togenerate a credential block 120 and store the credential block 120 in adata storage system 124. After computing apparatus validates userqualifications in the credential data structure 112, processor 104generates a credential block 120 and stores the credential block 120 ina data storage system 124. Each credential block may contain a specificuser qualification. Data storage system 124 includes an immutablesequential listing, discussed in further detail in FIG. 2 . Blocks ofthe immutable sequential listing 200 may be hashed and encoded into aMerkle tree. In an embodiment, each block includes the cryptographichash of the prior block, linking the blocks and creating a chain. Thetop of the Merkle tree may comprise a Merkle root that may comprise acryptographic accumulator 300. The immutable sequential listing 200includes a cryptographic accumulator 300, discussed in further detail inFIG. 3 . A “cryptographic accumulator,” as used in this disclosure, is adata structure created by relating a commitment, which may be smalleramount of data that may be referred to as an “accumulator” and/or“root,” to a set of elements, such as lots of data and/or collection ofdata, together with short membership and/or nonmembership proofs for anyelement in the set. In an embodiment, these proofs may be publiclyverifiable against the commitment. An accumulator may be said to be“dynamic” if the commitment and membership proofs can be updatedefficiently as elements are added or removed from the set, at unit costindependent of the number of accumulated elements; an accumulator forwhich this is not the case may be referred to as “static.” A membershipproof may be referred to as a “witness” whereby an element existing inthe larger amount of data can be shown to be included in the root, whilean element not existing in the larger amount of data can be shown not tobe included in the root, where “inclusion” indicates that the includedelement was a part of the process of generating the root, and thereforewas included in the original larger data set. Credential block 120 maycontain information that states that the credential is validated by theprocessor 104 and/or third-party validator 116. In an embodiment,information may include name of third-party validator 116, time ofvalidation, and the like. Credential block 120 includes a timestampshowing the time that the credential was verified. Time may also includethe date of verification. As used in this disclosure, a “timestamp” isan element of data stored in each block as a unique serial and whosemain function is to determine the exact moment in which the block hasbeen mined and validated by the apparatus.

Continuing to reference FIG. 1 , data storage system 124 is accessibleby a plurality of user devices. In an embodiment, employers may accessthe data storage system 124 to see the verified credential blocks of agiven employee. In another embodiment, job offerers may access the datastorage system 124 to ensure that a job seeker's credentials arevalidated. In an embodiment, users may view the timestamp of theverified credential block to see when a user qualification was verified.Users may download the data storage system 124 on to user devices tohave a record of verified credentials.

Referring now to FIG. 2 , an exemplary embodiment of an immutablesequential listing 200 is illustrated. Data elements are listing inimmutable sequential listing 200; data elements may include any form ofdata, including textual data, image data, encrypted data,cryptographically hashed data, and the like. Data elements may include,without limitation, one or more at least a digitally signed assertions.In one embodiment, a digitally signed assertion 204 is a collection oftextual data signed using a secure proof as described in further detailabove; secure proof may include, without limitation, a digital signatureas described above. Collection of textual data may contain any textualdata, including without limitation American Standard Code forInformation Interchange (ASCII), Unicode, or similar computer-encodedtextual data, any alphanumeric data, punctuation, diacritical mark, orany character or other marking used in any writing apparatus to conveyinformation, in any form, including any plaintext or cyphertext data; inan embodiment, collection of textual data may be encrypted, or may be ahash of other data, such as a root or node of a Merkle tree or hashtree, or a hash of any other information desired to be recorded in somefashion using a digitally signed assertion 204. In an embodiment,collection of textual data states that the owner of a certaintransferable item represented in a digitally signed assertion 204register is transferring that item to the owner of an address. Adigitally signed assertion 204 may be signed by a digital signaturecreated using the private key associated with the owner's public key, asdescribed above.

Still referring to FIG. 2 , a digitally signed assertion 204 maydescribe a transfer of virtual currency, such as crypto-currency asdescribed below. The virtual currency may be a digital currency. Item ofvalue may be a transfer of trust, for instance represented by astatement vouching for the identity or trustworthiness of the firstentity. Item of value may be an interest in a fungible negotiablefinancial instrument representing ownership in a public or privatecorporation, a creditor relationship with a governmental body or acorporation, rights to ownership represented by an option, derivativefinancial instrument, commodity, debt-backed security such as a bond ordebenture or other security as described in further detail below. Aresource may be a physical machine e.g. a ride share vehicle or anyother asset. A digitally signed assertion 204 may describe the transferof a physical good; for instance, a digitally signed assertion 204 maydescribe the sale of a product. In some embodiments, a transfernominally of one item may be used to represent a transfer of anotheritem; for instance, a transfer of virtual currency may be interpreted asrepresenting a transfer of an access right; conversely, where the itemnominally transferred is something other than virtual currency, thetransfer itself may still be treated as a transfer of virtual currency,having value that depends on many potential factors including the valueof the item nominally transferred and the monetary value attendant tohaving the output of the transfer moved into a particular user'scontrol. The item of value may be associated with a digitally signedassertion 204 by means of an exterior protocol, such as the COLOREDCOINS created according to protocols developed by The Colored CoinsFoundation, the MASTERCOIN protocol developed by the MastercoinFoundation, or the ETHEREUM platform offered by the Stiftung EthereumFoundation of Baar, Switzerland, the Thunder protocol developed byThunder Consensus, or any other protocol.

Still referring to FIG. 2 , in one embodiment, an address is a textualdatum identifying the recipient of virtual currency or another item ofvalue in a digitally signed assertion 204. In some embodiments, addressis linked to a public key, the corresponding private key of which isowned by the recipient of a digitally signed assertion 204. Forinstance, address may be the public key. Address may be arepresentation, such as a hash, of the public key. Address may be linkedto the public key in memory of a processor 104, for instance via a“wallet shortener” protocol. Where address is linked to a public key, atransferee in a digitally signed assertion 204 may record a subsequent adigitally signed assertion 204 transferring some or all of the valuetransferred in the first a digitally signed assertion 204 to a newaddress in the same manner. A digitally signed assertion 204 may containtextual information that is not a transfer of some item of value inaddition to, or as an alternative to, such a transfer. For instance, asdescribed in further detail below, a digitally signed assertion 204 mayindicate a confidence level associated with a distributed storage nodeas described in further detail below.

In an embodiment, and still referring to FIG. 2 immutable sequentiallisting 200 records a series of at least a posted content in a way thatpreserves the order in which the at least a posted content took place.Temporally sequential listing may be accessible at any of varioussecurity settings; for instance, and without limitation, temporallysequential listing may be readable and modifiable publicly, may bepublicly readable but writable only by entities and/or devices havingaccess privileges established by password protection, confidence level,or any device authentication procedure or facilities described herein,or may be readable and/or writable only by entities and/or deviceshaving such access privileges. Access privileges may exist in more thanone level, including, without limitation, a first access level orcommunity of permitted entities and/or devices having ability to read,and a second access level or community of permitted entities and/ordevices having ability to write; first and second community may beoverlapping or non-overlapping. In an embodiment, posted content and/orimmutable sequential listing 200 may be stored as one or more zeroknowledge sets (ZKS), Private Information Retrieval (PIR) structure, orany other structure that allows checking of membership in a set byquerying with specific properties. Such database may incorporateprotective measures to ensure that malicious actors may not query thedatabase repeatedly in an effort to narrow the members of a set toreveal uniquely identifying information of a given posted content.

Still referring to FIG. 2 , immutable sequential listing 200 maypreserve the order in which the at least a posted content took place bylisting them in chronological order; alternatively or additionally,immutable sequential listing 200 may organize digitally signedassertions 204 into sub-listings 208 such as “blocks” in a blockchain,which may be themselves collected in a temporally sequential order;digitally signed assertions 204 within a sub-listing 208 may or may notbe temporally sequential. The ledger may preserve the order in which atleast a posted content took place by listing them in sub-listings 208and placing the sub-listings 208 in chronological order. The immutablesequential listing 200 may be a distributed, consensus-based ledger,such as those operated according to the protocols promulgated by RippleLabs, Inc., of San Francisco, Calif., or the Stellar DevelopmentFoundation, of San Francisco, Calif., or of Thunder Consensus. In someembodiments, the ledger is a secured ledger; in one embodiment, asecured ledger is a ledger having safeguards against alteration byunauthorized parties. The ledger may be maintained by a proprietor, suchas an apparatus administrator on a server, that controls access to theledger; for instance, the user account controls may allow contributorsto the ledger to add at least a posted content to the ledger, but maynot allow any users to alter at least a posted content that have beenadded to the ledger. In some embodiments, ledger is cryptographicallysecured; in one embodiment, a ledger is cryptographically secured whereeach link in the chain contains encrypted or hashed information thatmakes it practically infeasible to alter the ledger without betrayingthat alteration has taken place, for instance by requiring that anadministrator or other party sign new additions to the chain with adigital signature. Immutable sequential listing 200 may be incorporatedin, stored in, or incorporate, any suitable data structure, includingwithout limitation any database, datastore, file structure, distributedhash table, directed acyclic graph or the like. In some embodiments, thetimestamp of an entry is cryptographically secured and validated viatrusted time, either directly on the chain or indirectly by utilizing aseparate chain. In one embodiment the validity of timestamp is providedusing a time stamping authority as described in the RFC 3161 standardfor trusted timestamps, or in the ANSI ASC x9.95 standard. In anotherembodiment, the trusted time ordering is provided by a group of entitiescollectively acting as the time stamping authority with a requirementthat a threshold number of the group of authorities sign the timestamp.

In some embodiments, and with continued reference to FIG. 2 , immutablesequential listing 200, once formed, may be inalterable by any party, nomatter what access rights that party possesses. For instance, immutablesequential listing 200 may include a hash chain, in which data is addedduring a successive hashing process to ensure non-repudiation. Immutablesequential listing 200 may include a block chain. In one embodiment, ablock chain is immutable sequential listing 200 that records one or morenew at least a posted content in a data item known as a sub-listing 208or “block.” An example of a block chain is the BITCOIN block chain usedto record BITCOIN transactions and values. Sub-listings 208 may becreated in a way that places the sub-listings 208 in chronological orderand link each sub-listing 208 to a previous sub-listing 208 in thechronological order so that any processor 104 may traverse thesub-listings 208 in reverse chronological order to verify any at least aposted content listed in the block chain. Each new sub-listing 208 maybe required to contain a cryptographic hash describing the previoussub-listing 208. In some embodiments, the block chain contains a singlefirst sub-listing 208 sometimes known as a “genesis block.”

Still referring to FIG. 2 , the creation of a new sub-listing 208 may becomputationally expensive; for instance, the creation of a newsub-listing 208 may be designed by a “proof of work” protocol acceptedby all participants in forming the immutable sequential listing 200 totake a powerful set of processors a certain period of time to produce.Where one sub-listing 208 takes less time for a given set of processorsto produce the sub-listing 208 protocol may adjust the algorithm toproduce the next sub-listing 208 so that it will require more steps;where one sub-listing 208 takes more time for a given set of processorsto produce the sub-listing 208 protocol may adjust the algorithm toproduce the next sub-listing 208 so that it will require fewer steps. Asan example, protocol may require a new sub-listing 208 to contain acryptographic hash describing its contents; the cryptographic hash maybe required to satisfy a mathematical condition, achieved by having thesub-listing 208 contain a number, called a nonce, whose value isdetermined after the fact by the discovery of the hash that satisfiesthe mathematical condition. Continuing the example, the protocol may beable to adjust the mathematical condition so that the discovery of thehash describing a sub-listing 208 and satisfying the mathematicalcondition requires more or less steps, depending on the outcome of theprevious hashing attempt. Mathematical condition, as an example, mightbe that the hash contains a certain number of leading zeros and ahashing algorithm that requires more steps to find a hash containing agreater number of leading zeros, and fewer steps to find a hashcontaining a lesser number of leading zeros. In some embodiments,production of a new sub-listing 208 according to the protocol is knownas “mining.” The creation of a new sub-listing 208 may be designed by a“proof of stake” protocol as will be apparent to those skilled in theart upon reviewing the entirety of this disclosure.

Continuing to refer to FIG. 2 , in some embodiments, protocol alsocreates an incentive to mine new sub-listings 208. The incentive may befinancial; for instance, successfully mining a new sub-listing 208 mayresult in the person or entity that mines the sub-listing 208 receivinga predetermined amount of currency. The currency may be fiat currency.Currency may be cryptocurrency as defined below. In other embodiments,incentive may be redeemed for particular products or services; theincentive may be a gift certificate with a particular business, forinstance. In some embodiments, incentive is sufficiently attractive tocause participants to compete for the incentive by trying to race eachother to the creation of sub-listings 208 Each sub-listing 208 createdin immutable sequential listing 200 may contain a record or at least aposted content describing one or more addresses that receive anincentive, such as virtual currency, as the result of successfullymining the sub-listing 208.

With continued reference to FIG. 2 , where two entities simultaneouslycreate new sub-listings 208, immutable sequential listing 200 maydevelop a fork; protocol may determine which of the two alternatebranches in the fork is the valid new portion of the immutablesequential listing 200 by evaluating, after a certain amount of time haspassed, which branch is longer. “Length” may be measured according tothe number of sub-listings 208 in the branch. Length may be measuredaccording to the total computational cost of producing the branch.Protocol may treat only at least a posted content contained the validbranch as valid at least a posted content. When a branch is foundinvalid according to this protocol, at least a posted content registeredin that branch may be recreated in a new sub-listing 208 in the validbranch; the protocol may reject “double spending” at least a postedcontent that transfer the same virtual currency that another at least aposted content in the valid branch has already transferred. As a result,in some embodiments the creation of fraudulent at least a posted contentrequires the creation of a longer immutable sequential listing 200branch by the entity attempting the fraudulent at least a posted contentthan the branch being produced by the rest of the participants; as longas the entity creating the fraudulent at least a posted content islikely the only one with the incentive to create the branch containingthe fraudulent at least a posted content, the computational cost of thecreation of that branch may be practically infeasible, guaranteeing thevalidity of all at least a posted content in the immutable sequentiallisting 200.

Still referring to FIG. 2 , additional data linked to at least a postedcontent may be incorporated in sub-listings 208 in the immutablesequential listing 200; for instance, data may be incorporated in one ormore fields recognized by block chain protocols that permit a person orcomputer forming a at least a posted content to insert additional datain the immutable sequential listing 200. In some embodiments, additionaldata is incorporated in an unspendable at least a posted content field.For instance, the data may be incorporated in an OP_RETURN within theBITCOIN block chain. In other embodiments, additional data isincorporated in one signature of a multi-signature at least a postedcontent. In an embodiment, a multi-signature at least a posted contentis at least a posted content to two or more addresses. In someembodiments, the two or more addresses are hashed together to form asingle address, which is signed in the digital signature of the at leasta posted content. In other embodiments, the two or more addresses areconcatenated. In some embodiments, two or more addresses may be combinedby a more complicated process, such as the creation of a Merkle tree orthe like. In some embodiments, one or more addresses incorporated in themulti-signature at least a posted content are typical crypto-currencyaddresses, such as addresses linked to public keys as described above,while one or more additional addresses in the multi-signature at least aposted content contain additional data related to the at least a postedcontent; for instance, the additional data may indicate the purpose ofthe at least a posted content, aside from an exchange of virtualcurrency, such as the item for which the virtual currency was exchanged.In some embodiments, additional information may include networkstatistics for a given node of network, such as a distributed storagenode, e.g. the latencies to nearest neighbors in a network graph, theidentities or identifying information of neighboring nodes in thenetwork graph, the trust level and/or mechanisms of trust (e.g.certificates of physical encryption keys, certificates of softwareencryption keys, (in non-limiting example certificates of softwareencryption may indicate the firmware version, manufacturer, hardwareversion and the like), certificates from a trusted third-party,certificates from a decentralized anonymous authentication procedure,and other information quantifying the trusted status of the distributedstorage node) of neighboring nodes in the network graph, IP addresses,GPS coordinates, and other information informing location of the nodeand/or neighboring nodes, geographically and/or within the networkgraph. In some embodiments, additional information may include historyand/or statistics of neighboring nodes with which the node hasinteracted. In some embodiments, this additional information may beencoded directly, via a hash, hash tree or other encoding.

With continued reference to FIG. 2 , in some embodiments, virtualcurrency is traded as a crypto-currency. In one embodiment, acrypto-currency is a digital, currency such as Bitcoins, Peercoins,Namecoins, and Litecoins. Crypto-currency may be a clone of anothercrypto-currency. The crypto-currency may be an “alt-coin.”Crypto-currency may be decentralized, with no particular entitycontrolling it; the integrity of the crypto-currency may be maintainedby adherence by its participants to established protocols for exchangeand for production of new currency, which may be enforced by softwareimplementing the crypto-currency. Crypto-currency may be centralized,with its protocols enforced or hosted by a particular entity. Forinstance, crypto-currency may be maintained in a centralized ledger, asin the case of the XRP currency of Ripple Labs, Inc., of San Francisco,Calif. In lieu of a centrally controlling authority, such as a nationalbank, to manage currency values, the number of units of a particularcrypto-currency may be limited; the rate at which units ofcrypto-currency enter the market may be managed by a mutuallyagreed-upon process, such as creating new units of currency whenmathematical puzzles are solved, the degree of difficulty of the puzzlesbeing adjustable to control the rate at which new units enter themarket. Mathematical puzzles may be the same as the algorithms used tomake productions of sub-listings 208 in a block chain computationallychallenging; the incentive for producing sub-listings 208 may includethe grant of new crypto-currency to the miners. Quantities ofcrypto-currency may be exchanged using at least a posted content asdescribed above.

Turning now to FIG. 3 , an exemplary embodiment of a cryptographicaccumulator 300 is illustrated. Cryptographic accumulator 300 has aplurality of accumulated elements 304, each accumulated element 304generated from a lot of the plurality of data lots. Accumulated elements304 are create using an encryption process, defined for this purpose asa process that renders the lots of data unintelligible from theaccumulated elements 304; this may be a one-way process such as acryptographic hashing process and/or a reversible process such asencryption. Cryptographic accumulator 300 further includes structuresand/or processes for conversion of accumulated elements 304 to root 312element. For instance, and as illustrated for exemplary purposes in FIG.3 , cryptographic accumulator 300 may be implemented as a Merkle treeand/or hash tree, in which each accumulated element 304 created bycryptographically hashing a lot of data. Two or more accumulatedelements 304 may be hashed together in a further cryptographic hashingprocess to produce a node 308 element; a plurality of node 308 elementsmay be hashed together to form parent nodes 308, and ultimately a set ofnodes 308 may be combined and cryptographically hashed to form root 312.Contents of root 312 may thus be determined by contents of nodes 308used to generate root 312, and consequently by contents of accumulatedelements 304, which are determined by contents of lots used to generateaccumulated elements 304. As a result of collision resistance andavalanche effects of hashing algorithms, any change in any lot,accumulated element 304, and/or node 308 is virtually certain to cause achange in root 312; thus, it may be computationally infeasible to modifyany element of Merkle and/or hash tree without the modification beingdetectable as generating a different root 312. In an embodiment, anyaccumulated element 304 and/or all intervening nodes 308 betweenaccumulated element 304 and root 312 may be made available withoutrevealing anything about a lot of data used to generate accumulatedelement 304; lot of data may be kept secret and/or demonstrated with asecure proof as described below, preventing any unauthorized party fromacquiring data in lot.

Alternatively or additionally, and still referring to FIG. 3 ,cryptographic accumulator 300 may include a “vector commitment” whichmay act as an accumulator in which an order of elements in set ispreserved in its root 312 and/or commitment. In an embodiment, a vectorcommitment may be a position binding commitment and can be opened at anyposition to a unique value with a short proof (sublinear in the lengthof the vector). A Merkle tree may be seen as a vector commitment withlogarithmic size openings. Subvector commitments may include vectorcommitments where a subset of the vector positions can be opened in asingle short proof (sublinear in the size of the subset). Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various alternative or additional cryptographic accumulators300 that may be used as described herein. In addition to Merkle trees,accumulators may include without limitation RSA accumulators, classgroup accumulators, and/or bi-linear pairing-based accumulators. Anyaccumulator may operate using one-way functions that are easy to verifybut infeasible to reverse, i.e. given an input it is easy to produce anoutput of the one-way function, but given an output it iscomputationally infeasible and/or impossible to generate the input thatproduces the output via the one-way function. For instance, and by wayof illustration, a Merkle tree may be based on a hash function asdescribed above. Data elements may be hashed and grouped together. Then,the hashes of those groups may be hashed again and grouped together withthe hashes of other groups; this hashing and grouping may continue untilonly a single hash remains. As a further non-limiting example, RSA andclass group accumulators may be based on the fact that it is infeasibleto compute an arbitrary root of an element in a cyclic group of unknownorder, whereas arbitrary powers of elements are easy to compute. A dataelement may be added to the accumulator by hashing the data elementsuccessively until the hash is a prime number and then taking theaccumulator to the power of that prime number. The witness may be theaccumulator prior to exponentiation. Bi-linear paring-based accumulatorsmay be based on the infeasibility found in elliptic curve cryptography,namely that finding a number k such that adding P to itself k timesresults in Q is impractical, whereas confirming that, given 4 points P,Q, R, S, the point, P needs to be added as many times to itself toresult in Q as R needs to be added as many times to itself to result inS, can be computed efficiently for certain elliptic curves.

Referring now to FIG. 4 , an exemplary embodiment of method 400 ofcredentialing users across multiple devices. Step 405 of method 400includes receiving, by a processor 104, a credential data structure 112by use of an identifier. An identifier may include a public key.Credential data structure 112 may be received in any method as describedabove such as an identifier. Step 410 of method 400 includes verifying,by the processor 104, the credential data structure 112. Processor 104may utilize a third-party validator 116 to verify user qualifications inthe credential data structure 112. Step 415 to 435 may be used to verifythe credential data structure 112. Step 415 of method 400 includesparsing, by the processor, at least a credential from the credentialdata structure. Step 420 includes generating, by the processor, avalidator community set as a function of the at least a credential. Step425 includes transmitting, by the processor, a validation request to athird-party validator to a remote device. The third-party validator mayinclude a cryptographic module and may be operated by a job offeror.Step 430 includes, generating by the processor, a web crawling processthrough the third-party validator. Step 435 includes receiving, by theprocessor, a validation record from the remote device.

Now referring to FIG. 5 , an exemplary embodiment of a machine-learningmodule 500 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 504 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 508 given data provided as inputs 512;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 5 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 504 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 504 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 504 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 504 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 504 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 504 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data504 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 5 ,training data 504 may include one or more elements that are notcategorized; that is, training data 504 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 504 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 504 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 504 used by machine-learning module 500 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample, inputs comprising score ideals and/or talent and riskcalculation scores may result in an output of candidate groupings.

Further referring to FIG. 5 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 516. Training data classifier 516 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 500 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 504. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 516 may classify elements of training data tosub-categories of candidate groupings, wherein the sub-categories mayinclude categories associated to a plurality of attributes of a subjectperson profile.

Still referring to FIG. 5 , machine-learning module 500 may beconfigured to perform a lazy-learning process 520 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 504. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 504 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 5 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 524. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 524 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 524 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 504set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 5 , machine-learning algorithms may include atleast a supervised machine-learning process 528. At least a supervisedmachine-learning process 528, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude score ideals and/or talent and risk calculation scores asdescribed above as inputs, candidate groupings as outputs, and a scoringfunction representing a desired form of relationship to be detectedbetween inputs and outputs; scoring function may, for instance, seek tomaximize the probability that a given input and/or combination ofelements inputs is associated with a given output to minimize theprobability that a given input is not associated with a given output.Scoring function may be expressed as a risk function representing an“expected loss” of an algorithm relating inputs to outputs, where lossis computed as an error function representing a degree to which aprediction generated by the relation is incorrect when compared to agiven input-output pair provided in training data 504. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various possible variations of at least a supervisedmachine-learning process 528 that may be used to determine relationbetween inputs and outputs. Supervised machine-learning processes mayinclude classification algorithms as defined above.

Further referring to FIG. 5 , machine learning processes may include atleast an unsupervised machine-learning processes 532. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 5 , machine-learning module 500 may be designedand configured to create a machine-learning model 524 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 5 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includevarious forms of latent space regularization such as variationalregularization. Machine-learning algorithms may include Gaussianprocesses such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more processors that are utilized as a userprocessor for an electronic document, one or more server devices, suchas a document server, etc.) programmed according to the teachings of thepresent specification, as will be apparent to those of ordinary skill inthe computer art. Appropriate software coding can readily be prepared byskilled programmers based on the teachings of the present disclosure, aswill be apparent to those of ordinary skill in the software art. Aspectsand implementations discussed above employing software and/or softwaremodules may also include appropriate hardware for assisting in theimplementation of the machine executable instructions of the softwareand/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a processor) and thatcauses the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., aprocessor) and any related information (e.g., data structures and data)that causes the machine to perform any one of the methodologies and/orembodiments described herein.

Examples of a processor include, but are not limited to, an electronicbook reading device, a computer workstation, a terminal computer, aserver computer, a handheld device (e.g., a tablet computer, asmartphone, etc.), a web appliance, a network router, a network switch,a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a processor may include and/or beincluded in a kiosk.

FIG. 6 shows a diagrammatic representation of one embodiment of aprocessor in the exemplary form of a computer apparatus 600 within whicha set of instructions for causing a control apparatus to perform any oneor more of the aspects and/or methodologies of the present disclosuremay be executed. It is also contemplated that multiple processors may beutilized to implement a specially configured set of instructions forcausing one or more of the devices to perform any one or more of theaspects and/or methodologies of the present disclosure. Computerapparatus 600 includes a processor 604 and a memory 608 that communicatewith each other, and with other components, via a bus 612. Bus 612 mayinclude any of several types of bus structures including, but notlimited to, a memory bus, a memory controller, a peripheral bus, a localbus, and any combinations thereof, using any of a variety of busarchitectures.

Processor 604 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 604 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 604 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or apparatus on a chip (SoC).

Memory 608 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output apparatus 616 (BIOS), including basic routines that help totransfer information between elements within computer apparatus 600,such as during start-up, may be stored in memory 608. Memory 608 mayalso include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 620 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 608 may further include any number of program modulesincluding, but not limited to, an operating apparatus, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer apparatus 600 may also include a storage device 624. Examplesof a storage device (e.g., storage device 624) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 624 may beconnected to bus 612 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device624 (or one or more components thereof) may be removably interfaced withcomputer apparatus 600 (e.g., via an external port connector (notshown)). Particularly, storage device 624 and an associatedmachine-readable medium 628 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer apparatus 600. In one example,software 620 may reside, completely or partially, withinmachine-readable medium 628. In another example, software 620 mayreside, completely or partially, within processor 604.

Computer apparatus 600 may also include an input device 632. In oneexample, a user of computer apparatus 600 may enter commands and/orother information into computer apparatus 600 via input device 632.Examples of an input device 632 include, but are not limited to, analpha-numeric input device (e.g., a keyboard), a pointing device, ajoystick, a gamepad, an audio input device (e.g., a microphone, a voiceresponse apparatus, etc.), a cursor control device (e.g., a mouse), atouchpad, an optical scanner, a video capture device (e.g., a stillcamera, a video camera), a touchscreen, and any combinations thereof.Input device 632 may be interfaced to bus 612 via any of a variety ofinterfaces (not shown) including, but not limited to, a serialinterface, a parallel interface, a game port, a USB interface, aFIREWIRE interface, a direct interface to bus 612, and any combinationsthereof. Input device 632 may include a touch screen interface that maybe a part of or separate from display 636, discussed further below.Input device 632 may be utilized as a user selection device forselecting one or more graphical representations in a graphical interfaceas described above.

A user may also input commands and/or other information to computerapparatus 600 via storage device 624 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 640. A networkinterface device, such as network interface device 640, may be utilizedfor connecting computer apparatus 600 to one or more of a variety ofnetworks, such as network 644, and one or more remote devices 648connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two processors, and any combinations thereof. A network, such asnetwork 644, may employ a wired and/or a wireless mode of communication.In general, any network topology may be used. Information (e.g., data,software 620, etc.) may be communicated to and/or from computerapparatus 600 via network interface device 640.

Computer apparatus 600 may further include a video display adapter 662for communicating a displayable image to a display device, such asdisplay device 636. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 662 and display device 636 may beutilized in combination with processor 604 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer apparatus 600 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 612 via a peripheral interface 666.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,apparatuses, and software according to the present disclosure.Accordingly, this description is meant to be taken only by way ofexample, and not to otherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. An apparatus for cryptographic distributedstorage of credentials, the apparatus comprising: at least a processor;and a memory communicatively connected to the processor, the memorycontaining instructions configuring the at least a processor to: receivea credential data structure from a user by use of an identifier; verifythe credential data structure, wherein verifying further comprises:parsing at least a credential from the credential data structure;generating a validator community set as a function of the at least acredential, wherein the validator community set includes a plurality ofidentifiers; transmitting a validation request to a third-partyvalidator to a remote device associated with an identifier of theplurality of identifiers; generating a web crawling process for thethird-party validator; receiving a validation record from thethird-party validator of the remote device.
 2. The apparatus of claim 1,wherein verifying the credential data structure comprises using amachine-learning module to identify the validator community set.
 3. Theapparatus of claim 1, wherein the third-party validator comprises acryptographic module.
 4. The apparatus of claim 1, wherein thecredential data structure comprises the user's qualifications.
 5. Theapparatus of claim 1, wherein the third-party validator is operated by ajob offeror.
 6. The apparatus of claim 5, wherein the job offerorcomprises an individual who has worked with a job seeker in the past. 7.The apparatus of claim 1, wherein the processor is configured tovalidate a user identity.
 8. The apparatus of claim 1, wherein thecredential data structure comprises a digital wallet.
 9. The apparatusof claim 1, wherein the data storage system comprises an immutablesequential listing.
 10. The apparatus of claim 1, wherein the identifiercomprises a public key.
 11. A method for credentialing users acrossmultiple devices, the method comprising: receiving, by a processor, acredential data structure by use of an identifier; verifying, by theprocessor, the credential data structure; parsing, by the processor, atleast a credential from the credential data structure; generating, bythe processor, a validator community set as a function of the at least acredential, wherein the validator community set includes a plurality ofidentifiers; transmitting, by the processor, a validation request to athird-party validator to a remote device associated with an identifierof the plurality of identifiers; generating, by the processor, a webcrawling process for the third-party validator; receiving, by theprocessor, a validation record from the third-party validator of theremote device.
 12. The method of claim 11, wherein verifying thecredential data structure comprises using a machine-learning module toidentify the validator community set.
 13. The method of claim 11,wherein the third-party validator comprises a cryptographic module. 14.The method of claim 11, wherein the credential data structure comprisesthe user's qualifications.
 15. The method of claim 14, wherein thethird-party validator is operated by a job offeror.
 16. The method ofclaim 15, wherein the job offeror comprises an individual who has workedwith a job seeker in the past.
 17. The method of claim 11, wherein theprocessor is configured to validate a user identity.
 18. The method ofclaim 11, wherein the credential data structure comprises a digitalwallet.
 19. The method of claim 11, wherein the data storage systemcomprises an immutable sequential listing.
 20. The method of claim 11,wherein the identifier comprises a public key.